bibguard 0.2.0

Detect hallucinated and broken citations in academic papers

bibguard

Detect hallucinated and broken citations in academic papers.

One command to verify every reference in your .bib file against five scholarly databases. Catches phantom DOIs, fabricated arXiv IDs, author mismatches, retracted papers, and AI-hallucinated citations.

pip install bibguard
bibguard paper.bib

Landing Page | PyPI | Changelog

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Why

Large language models hallucinate citations. Copy-paste errors corrupt metadata. Retracted papers slip through review. bibguard catches these problems before submission.

• 5 sources: arXiv, Crossref, DBLP, Semantic Scholar, OpenAlex
• Phantom ID detection: Valid-format DOI/arXiv that doesn't resolve = hallucination signal
• Kill-shot logic: A phantom ID cannot be overridden by a similar search result
• TeX cross-audit: Find \cite{key} with no .bib entry, and orphan entries never cited
• Duplicate detection: Flag near-identical entries with different keys
• Auto-fix: Generate a corrected .bib with missing DOIs and eprint IDs filled in
• Zero heavy dependencies: Core requires only requests + bibtexparser

Install

pip install bibguard            # minimal
pip install bibguard[fast]      # + RapidFuzz for better title matching
pip install bibguard[all]       # + RapidFuzz + PyMuPDF for PDF parsing

Requires Python 3.9+.

Usage

CLI

# Basic: verify all entries in a .bib file
bibguard references.bib

# With TeX cross-audit (finds phantom \cite and orphan entries)
bibguard references.bib --tex main.tex

# Save report + auto-fix
bibguard references.bib --tex main.tex --out report.md --fix fixed.bib

# JSON output (for CI pipelines)
bibguard references.bib --json --out report.json

Python API

from bibguard import verify_bib, verify_entry

# Verify entire .bib file
results, report = verify_bib("references.bib", tex_path="main.tex")
for r in results:
    if r.overall != "OK":
        print(f"{r.overall}: {r.key} -- {r.title}")

# Verify a single entry
from bibguard.parsers.bibtex import parse_bib
entries = parse_bib("references.bib")
result = verify_entry(entries[0])
print(result.overall, result.checks)

Exit codes

Code	Meaning
0	All entries OK or WARN
1	At least one FAIL (hallucination, phantom ID, no match, or retraction)
2	Input error (file not found)

Use in CI: bibguard references.bib || echo "Citation issues found"

How it works

Input (.bib)
  |
  v
Parse entries (bibtexparser)
  |
  v
For each entry:
  1. arXiv lookup (by arXiv ID)        -- direct ID resolution
  2. Crossref lookup (by DOI)           -- direct ID resolution
  3. Phantom ID detection               -- valid format but doesn't resolve?
  4. DBLP search (by title + author)    -- with author disambiguation
  5. Semantic Scholar search             -- fallback + citation count
  6. OpenAlex search                     -- fallback for non-CS / old papers
  |
  v
Post-processing:
  - Source-aware status (confirmed source overrides noisy cross-checks)
  - Kill-shot: phantom ID overrides search-confirmed status
  - Suggested fixes (missing DOI, eprint)
  |
  v
Report (Markdown / JSON)
  + TeX cross-audit (phantom refs, orphan entries)
  + Duplicate detection
  + Auto-fixed .bib

Benchmark

Tested on a 58-case golden test set (tests/golden_benchmark.bib). Reproduce with python tests/bench_golden.py.

Category	Metric	Result
Hallucinated (14 fabricated + 1 control)	Detected as FAIL	14/14 (100%)
	Detected as >= WARN	15/15 (100%)
Chimera (5 mixed-metadata)	Detected as >= WARN	5/5 (100%)
Real papers (10 legitimate)	Clean pass (OK)	4/10 (40%)
	False positive (FAIL)	1/10 (10%)
Retracted (28 retractions)	Any issue flagged	19/28 (68%)
Runtime	58 entries	95s (~1.6s/entry)

Notes:

• The 1 false-positive FAIL is a 1962 book with no DOI — a known edge case for API-based verification.
• WARN on real papers are mostly venue/author-count mismatches — reviewable by humans.
• All 14 truly fabricated citations are caught at FAIL level.

For semantic NLI, citation graph analysis, and Bayesian risk scoring, see IntegriRef.

AI Coding Assistant Integration

bibguard ships with skill/rule definitions for major AI coding assistants. The AI runs verification, analyzes FAIL entries, and searches the web to find correct references.

Claude Code

mkdir -p ~/.claude/commands
curl -o ~/.claude/commands/bibguard.md \
  https://raw.githubusercontent.com/GeoffreyWang1117/bibguard/main/.claude/commands/bibguard.md

Then use /bibguard paper.bib in Claude Code.

OpenAI Codex CLI

mkdir -p ~/.codex/skills/bibguard
curl -o ~/.codex/skills/bibguard/SKILL.md \
  https://raw.githubusercontent.com/GeoffreyWang1117/bibguard/main/.codex/skills/bibguard/SKILL.md

Cursor

mkdir -p .cursor/rules
curl -o .cursor/rules/bibguard.md \
  https://raw.githubusercontent.com/GeoffreyWang1117/bibguard/main/.cursor/rules/bibguard.md

Any other assistant

bibguard is a standard CLI tool. Any AI assistant that can run shell commands can use it:

bibguard paper.bib --json --out report.json

Optional dependencies

Package	Purpose	Install
rapidfuzz	Better title matching (token_set_ratio)	pip install bibguard[fast]
pymupdf	PDF reference extraction (no GROBID needed)	pip install bibguard[pdf]

API sources

Source	Lookup method	Coverage
arXiv	ID resolution	CS, Physics, Math, ...
Crossref	DOI resolution	150M+ records
DBLP	Title search	CS papers (gold standard)
Semantic Scholar	Title search	200M+ papers
OpenAlex	Title search	250M+ works (all disciplines)

All queries respect rate limits. No API keys required.

Contributing

Issues and PRs welcome. To run tests:

git clone https://github.com/GeoffreyWang1117/bibguard.git
cd bibguard
pip install -e ".[dev]"
pytest

Related

• IntegriRef -- Full L0-L4 verification stack with semantic NLI (93.5% accuracy), citation graph analysis, and Bayesian risk scoring
• Rebiber -- Normalize BibTeX with DBLP/ACL Anthology

Contributors

See CONTRIBUTORS.md for detailed attribution.

• Geoffrey Wang — Architecture, core algorithms, phantom-ID detection, kill-shot logic, benchmark design
• Claude (Anthropic) — Modular refactoring, output formatting, packaging, documentation

License

Apache License 2.0. See LICENSE for details.

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中文说明

bibguard — 学术论文引用幻觉检测工具

一行命令，检测论文中的虚假引用。

大语言模型会"幻觉"出格式正确但根本不存在的参考文献。bibguard 用 5 个学术数据库（arXiv、Crossref、DBLP、Semantic Scholar、OpenAlex）交叉验证你的 .bib 文件。

pip install bibguard
bibguard paper.bib

核心能力

• 幽灵 DOI / arXiv ID 检测 — 格式正确但不存在 = 最强幻觉信号
• Kill-shot 逻辑 — 幽灵 ID 不会被相似论文的搜索结果覆盖
• TeX 交叉审计 — 检测 \cite{key} 在 .bib 中无定义，以及定义了但从未引用的条目
• 自动修复 — 补全缺失的 DOI 和 eprint 字段
• 极轻依赖 — 仅需 requests + bibtexparser

基准测试 (58 条公开测试集)

类别	指标	结果
AI 幻觉引用 (14 条)	检出率 (FAIL)	100%
元数据嵌合体 (5 条)	检出率 (>=WARN)	100%
真实论文 (10 条)	假阳率 (FAIL)	10% (1 本 1962 年书籍)
运行时间	58 条	95 秒

AI 编程助手集成

支持 Claude Code (/bibguard)、OpenAI Codex、Cursor 自动触发。

深度验证

bibguard 是 L0（存在性验证）层。如需语义 NLI 验证 (93.5%)、引文图谱异常检测、贝叶斯风险评分，请使用 IntegriRef。